Continental Shelf Research 20 (2000) 373}387 Temporal and spatial variations of sea surface temperature in the East China Sea Chente Tseng*, Chiyuan Lin, Shihchin Chen, Chungzen Shyu Department of Fishery Information, Taiwan Fisheries Research Institute, Keelung, Taiwan, ROC Abstract Sea surface temperature of the East China Sea (ECS) were analyzed using the NOAA/AVHRR SST images. These satellite images reveal surface features of ECS including mainly the Kuroshio Current, Kuroshio Branch Current, Taiwan Warm Current, China coastal water, Changjiang diluted water and Yellow Sea mixed cold water. The SST of ECS ranges from 27 to 293C in summer; some cold eddies were found o! northeast Taiwan and to the south of Changjiang mouth. SST anomalies at the center of these eddies were about 2}53C. The strongest front usually occurs in May each year and its temperature gradient is about 5}63C over a cross-shelf distance of 30 nautical miles. The Yellow Sea mixed cold water also provides a contrast from China Coastal waters shoreward of the 50 m isobath; cross-shore temperature gradient is about 6}83C over 30 nautical miles. The Kuroshio intrudes into ECS preferably at two locations. The "rst is o! northeast Taiwan; the subsurface water of Kuroshio is upwelled onto the shelf while the main current is de#ected seaward. The second site is located at 313N and 1283E, which is generally considered as the origin of the Tsushima Warm Current. More quantitatively, a 2-year time series of monthly SST images is examined using EOF analysis to determine the spatial and temporal variations in the northwestern portion of ECS. The "rst spatial EOF mode accounts for 47.4% of total spatial variance and reveals the Changjiang plume and coastal cold waters o! China. The second and third EOF modes account for 16.4 and 9.6% of total variance, respectively, and their eigenvector images show the intrusion of Yellow Sea mixed cold waters and the China coastal water. The fourth EOF mode accounts for 5.4% of total variance and reveals cold eddies around Chusan Islands. The temporal variance EOF analysis is less revealing in this study area. ( 2000 Elsevier Science Ltd. All rights reserved. Keywords: East China Sea; NOAA/AVHRR; SST; EOF analysis; Kuroshio; Satellite remote sensing * Corresponding author. 0278-4343/00/$- see front matter ( 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 8 - 4 3 4 3 ( 9 9 ) 0 0 0 7 7 - 1 374 C. Tseng et al. / Continental Shelf Research 20 (2000) 373}387 1. Introduction The East China Sea (ECS) is a broad continental shelf bounding the North Paci"c Ocean in the west (see Fig. 1). It is connected to the Yellow Sea (YS) in the north and the South China Sea (SCS) in the south through the Taiwan Strait. The broad shelf of ECS, covering an area of about 770,000 km2, is bounded seaward by the 200 m isobath extending from northeast Taiwan to southern Japan (Fig. 1). Farther seaward is the steep continental slope over which the Kuroshio #ows. Major surface features of ECS include the Kuroshio Current (KC), the Kuroshio Branch Current (KBC), the Taiwan Warm Current (TWC), the China Coastal water (CCW), the Changjiang (Yangtze River) water and the Yellow Sea mixed cold water. The temporal and spatial variations of these systems and interactions among them are a!ected mainly by the East Asian monsoon. In addition, bottom topographic features may also modulate Fig. 1. East China Sea and vicinity. Isobaths are in meters. C. Tseng et al. / Continental Shelf Research 20 (2000) 373}387 375 the structures of thermohaline fronts, cold eddies, meanders and thermocline depths (Miao and Yu, 1991). Distributions of nutrients, planktons, chlorophyll-a, dissolved oxygen, "sh larvae and species are a!ected by these mesoscale features. In recent years, two interdisciplinary research projects including the KEEP (Kuroshio Edge Exchange Processes) series initiated by Taiwan and the `China}Japan Joint Investigation and Study on the Kuroshioa have been conducted. Physical and biogeochemical data provide a "rst-order description of basic processes at work. These processes include the seasonal variation of the Kuroshio main axis and its relation to fronts and eddies, and the Kuroshio countercurrent variation in relation to intrusions of Kuroshio subsurface waters onto ECS o! northeast Taiwan (Sun and Pan, 1987; Qiu et al., 1990; Chao, 1991; Lin et al., 1992; Tang and Wen, 1994). Furthermore, the dispersal of Changjiang River plume and associated suspended sediments into ECS has been reported by Beardsley et al. (1985). The Changjiang runo! and distributed freshwater discharge from China often form a narrow band of cold waters shoreward of the 50 m isobath (Pan et al., 1991a,b). The origin and axis variation of TWC was also documented by in situ hydrographic data (Pan et al., 1987). It was also found that the Yellow Sea mixed cold waters often intrude southward into ECS and interact with the Kuroshio to form fronts, eddies, meanders, and cold and warm core rings (Zheng and Klemas, 1982; Yuxiang, 1996). Lacking synopticity covering the entire ECS, major components of KEEP studies mainly concentrated on regional dynamics in the southern portion of ECS. The present study complement their e!orts by providing a synoptic view of the entire ECS. We utilize a time series of sea surface temperature (SST) images obtained from synoptic view NOAA weather satellite advanced very high resolution radiometer (AVHRR) to examine the relation between SST and regional processes reported earlier or in the KEEP series special issue. Moreover, the EOF analysis was used to decompose the multidimensional data into modes ranked by their variance. From those EOF modes, we could identify the dominant feature components in the study area. In the past decades, the EOF method had been applied to analyze the AVHRR/MCSST sea surface temperature images sequence to examine some ocean processes, such as large- and mesoscale current systems (Gallaudet and Simpson, 1994), upwelling and cold eddies (Fang and Hsieh, 1993), seasonal and annual SST variability (Chiswell, 1994; Yu and Emery, 1996) and meander propagation (Everson et al., 1997), etc. This method will be discussed brie#y in the next section. 2. Material and methods 2.1. NOAA/AVHRR SST images The NOAA/HRPT (High Resolution Picture Transmission) station of the Satellite Remote Sensing Laboratory in Taiwan Fisheries Research Institute (TFRI) is located in Keelung, Taiwan (location: 25.13N, 121.73E; height: 25 m above sea level). The station operates daily to receive and process the NOAA/AVHRR data. In the study period (1994/10}1996/09), SST "elds of ECS were available from NOAA-9, 12 and 14 376 C. Tseng et al. / Continental Shelf Research 20 (2000) 373}387 Table 1 The characteristics of NOAA/AVHRR (Advanced Very High Resolution Radiometer)! Channel No. Wavelength (lm) Visible 1 0.58}0.68 2 0.725}1.1 Infrared 3 3.55}3.93 4 10.3}11.3 5 11.5}12.5 IFOV at nadir 1.1 k]1.1 km Swath width 2580 km NEDT 0.123C Height of orbit 850 km !IFOV: Instantaneous "eld of view. NEDT: Noise equivalent di!erential temperature. satellites. The AVHRR carried aboard the NOAA polar orbiting satellites is equipped with a "ve-channel sensor in the visible and infrared wavebands. Table 1 shows the main characteristics of the AVHRR (Lauritson et al., 1979). The AVHRR data are extracted from the raw HRPT telemetry data to calculate the digital SST images by using the MultiChannel Sea Surface Temperature (MCSST) method (Strong and McClain, 1984; McClain et al., 1985). Those SST images are in 1.1]1.1 km2 resolu- tion (IFOV at nadir) and its noise equivalent di!erential temperature (NEDT) is approximately 0.123C. During this study, hundreds of images were processed and 262 MCSST images that are mostly cloud-free were selected. Fig. 1 shows the topography of the East China Sea and geographical covering area of satellite image in this study. It is centered at 283N, 1253E and covers an area of approximately 970 km]880 km (880]800 pixels). All of the 262 images were used to analyze the temporal}spatial variations of surface features in this area. 2.2. EOF analysis In this study, the monthly SST images were utilized to extract dominant surface features through EOF analysis. Available cloud-free images in each month are averaged pixel by pixel to derive the monthly mean SST. In order to interpret the di!erence between, and suitability of, temporal and spatial variances, EOF is per- formed by two methods (Lagerloef and Berstein, 1988; Eslinger et al., 1989; SeaSpace, 1992; Gallaudet and Simpson, 1994; Kawamara, 1994). First, all SST images were pre-processed by removing the temporal average of all monthly images. The expres- sion is given as follows: 1 N I@(x, t)"I(x, t)! + I(x, t), (1) N t/1 C. Tseng et al. / Continental Shelf Research 20 (2000) 373}387 377 where I(x, t) represents the set of all monthly images, I@(x, t) is the set of all demeaned monthly images, and N is the number of all monthly images. Using EOF analysis the demeaned monthly images, 24 in total, produce the temporal variance. Alternatively, the spatial average of each image can be removed as follows: 1 M I@(x, t)"I(x, t)! + I(x, t), (2) Mx/1 where M is the total pixel number of every image. The spatial variance can then be examined using EOF analysis. The spatially or temporally demeaned I@(x, t) set is used to calculate the auto- variance of each image and the covariance between images.